Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006, Proceedings

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Springer Science & Business Media, Aug 29, 2006 - Business & Economics - 297 pages
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The second IAPR TC3 Workshop on Arti'cial Neural Networks in Pattern Recognition, ANNPR 2006, was held at the University of Ulm (Germany), - gust 31 - September 2, 2006. The Neural Networks and Computational Intel- gence (TC3) group is one of the 20 Technical Committees of the International AssociationforPatternRegognition(IAPR).ThescopeofTC3includesCom- tational Intelligence approaches, such as fuzzy systems, evolutionary computing and arti'cial neural networks in various pattern recognition applications. AN- NPR 2006 succeeded the outstanding ?rst ANNPR workshop held at the U- versity of Florence in September 2003 and focused on arti'cial neural networks inspired from pattern recognition tasks. In recent years, the ?eld of neural networks has matured considerably in both methodology and real-world applications. As re'ected in this book, art- cial neural networks in pattern recognition combine many ideas from machine learning,advancedstatistics,signalandimageprocessing,andstatisticalpattern recognition for solving complex real-world pattern recognition problems. High quality across such a diverse ?eld of research can only be achieved through a rigorous and selective review process. For this workshop, 49 papers were submitted out of which 26 were selected for inclusion in the proceedings. ANNPR 2006 featured research work in the areas of neural network learning - unsupervised, semi-supervised and supervised - support vector machines, mul- ple classi'er systems, pattern recognition in image processing, and data mining in bioinformatics.
 

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Contents

Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions
1
Comparison Between Two SpatioTemporal Organization Maps for Speech Recognition
11
Adaptive Feedback Inhibition Improves Pattern Discrimination Learning
21
Supervised Batch Neural Gas
33
Fuzzy Labeled SelfOrganizing Map with LabelAdjusted Prototypes
46
On the Effects of Constraints in Semisupervised Hierarchical Clustering
57
A Study of the Robustness of KNN Classifiers Trained Using Soft Labels
67
An Experimental Study on Training Radial Basis Functions by Gradient Descent
81
Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques
165
Multiple Classifier Systems for Embedded String Patterns
177
Multiple Neural Networks for Facial Feature Localization in OrientationFree Face Images
188
Hierarchical Neural Networks Utilising DempsterShafer Evidence Theory
198
A Comparison Among Statistical Methods and Stacked Generalization
210
Object Detection and Feature Base Learning with Sparse Convolutional Neural Networks
221
Visual Classification of Images by Learning Geometric Appearances Through Boosting
233
An Eye Detection System Based on Neural Autoassociators
244

A Local Tangent Space Alignment Based Transductive Classification Algorithm
93
Incremental Manifold Learning Via Tangent Space Alignment
107
A Convolutional Neural Network Tolerant of Synaptic Faults for LowPower Analog Hardware
122
Ammonium Estimation in a Biological Wastewater Plant Using Feedforward Neural Networks
133
Support Vector Regression Using Mahalanobis Kernels
144
Incremental Training of Support Vector Machines Using Truncated Hypercones
153
Orientation Histograms for Face Recognition
253
An Empirical Comparison of Feature Reduction Methods in the Context of Microarray Data Classification
260
Unsupervised Feature Selection for Biomarker Identification in Chromatography and Gene Expression Data
274
Learning and Feature Selection Using the Set Covering Machine with DataDependent Rays on Gene Expression Profiles
286
Author Index
298
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